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Why do we need statistics? Variability (atropine)  Effect of variability Large amount of data describing the same thing (many values for one variable) No certainty “Deterministic vs probabilistic” Sampling

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Descriptive statistics  Descriptive statistics is a series of procedures designed to illuminate the data, so that its principal characteristics and main features are revealed.  This may mean sorting the data by size; perhaps putting it into a table, may be presenting it in an appropriate chart, or summarizing it numerically; and so on.

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Descriptive statistics  Qualitative variables In terms of describing data, an appropriate chart is almost always a good idea. What ‘appropriate’ means depends primarily on the type of data, as well as on what particular features of it you want to explore. Finally, a chart can often be used to illustrate or explain a complex situation for which a form of words or a table might be clumsy, lengthy or otherwise inadequate.

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Inferential statistics (Informed guess)  Hypothesis testing  Almost all clinical research begins with a question.  For example, is stress a risk factor for breast cancer?  To answer questions like this you have to transform the research question into a testable hypothesis called the null hypothesis, conventionally labeled H 0.  This usually takes the following form: H 0 : Stress is NOT a risk factor for breast cancer H 0 : The drug has NO effect on mean heart rate

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Inferential statistics (Informed guess)  Hypothesis testing  Null hypotheses reflect the conservative position of no difference, no risk, no effect, etc.,  To test this null hypothesis, researchers will take samples and measure outcomes, and decide whether the data from the sample provides strong enough evidence to be able to reject the null hypothesis or not.  If evidence against the null hypothesis is strong enough for us to be able to reject it, then we are implicitly accepting that some specified alternative hypothesis, usually labelled H 1, is probably true.

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Inferential statistics (Informed guess)  Hypothesis testing  Example Let the mean heart rate of all people having Inderal is  1 Let the mean heart rate of all people having the other new drug is  2

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Inferential statistics (Informed guess)  Some example of testing of hypothesis? Comparisons  One sample  Two independent samples  Two dependent samples  More than two samples (independent-dependent)  Comparing two or more factors Association

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Final words  If valid data are analyzed improperly, then the results become invalid and the conclusions may well be inappropriate.  At best, the net effect is to waste time, effort, and money for the project.  At worst, therapeutic decisions may well be based upon invalid conclusions and patients’ wellbeing may be jeopardized.